PROJECT SUMMARY/ABSTRACT Heart failure (HF) is a debilitating disease that affects over five million people in the United States. Occurrence of, morbidity related to, and hospitalization due to HF have serious financial implications. In 2009, HF had a direct cost of over $34 billion annually, the majority of which was due to hospitalizations. By 2030, HF total direct costs are predicted to reach $53 billion, and indirect costs are predicted to rise from $31 billion to $70 billion. Increases in costs are contingent on the increase in the aging population, making prevention of HF and care efficiency imperative. Fifty percent of readmissions due to HF are preventable, with lack of adherence to prescribed self-care as the driving factor. Results of telemedicine intervention studies to support adherence to self-care and improve HF outcomes are inconclusive. Past telemedicine interventions for HF have utilized an array of methods including: wireless sensors, telephone services, websites, and home visits from nurses. Structured telephone support has shown in some cases to reduce hospitalization, improve clinical outcomes, and reduce all-cause mortality in HF patients. However, patient adherence to telemedicine interventions is often low. This lack of adherence is due in part to the high treatment burden placed upon patients in such home monitoring interventions, which require them to engage in novel behaviors, including using new unfamiliar hardware and spending time meeting with home health nurses. The goal of this R01 is to demonstrate the following: 1) patients are more adherent with a home monitoring regimen when using minimally-invasive monitoring technologies, including wrist-worn consumer activity trackers; 2) a minimally-invasive home monitoring regimen combined with novel predictive algorithms may be used to forecast hospital readmission; and 3) data from the electronic health record (EHR) and a baseline survey may be used to predict levels of adherence to the home monitoring regimen. Towards these goals, we will recruit 500 HF patients to participate in a minimally-invasive home monitoring regimen. A novel mobile application will allow patients to monitor their progress, submit additional data, and receive adherence notifications. We will measure levels of adherence to the regimen, and use collected sensor data and known readmission events to create a novel hidden semi-Markov model that continuously predicts readmission risk. Predicting a patient's level of adherence will be performed with EHR data and a baseline survey using several techniques, including logistic regression and support vector machine models. The work outlined in this proposal will produce a set of foundational tools for performing home monitoring of HF patients. These tools will be adaptable for future studies of individually-tailored interventions, towards our ultimate goal of allowing patients to download an ?app? from an ?app store? that adapts to their individual characteristics and allows them to more effectively manage their disease.